def train(train_loader, model, criteon, optimizer, epoch):
  train_loss = 0
  train_acc = 0
  num_correct= 0
  for step, (x,y) in enumerate(train_loader):

    # x: [b, 3, 224, 224], y: [b]
    x, y = x.to(device), y.to(device)

    model.train()
    logits = model(x)
    loss = criteon(logits, y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    train_loss += float(loss.item())
    train_losses.append(train_loss)
    pred = logits.argmax(dim=1)
    num_correct += torch.eq(pred, y).sum().float().item()
  logger.info("Train Epoch: {}\t Loss: {:.6f}\t Acc: {:.6f}".format(epoch,train_loss/len(train_loader),num_correct/len(train_loader.dataset)))
  return num_correct/len(train_loader.dataset), train_loss/len(train_loader)